\chapter{Introduction}
\label{chapter_introduction}

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\section{Motivation}

In the modern medicine, it is the physician's undertaking to make sure that the recovery time for every patient is as short as possible. This ensures maximum comfort and minimum distress for the patient, which in turn facilitates the creation of a stronger society. Keeping this in mind, intra-operative diagnosis has been a keen area of interest in medical technology since Wilhelm R\"{o}ntgen first took a look inside the human body. In essence, a method which can help surgeons decide the best surgical approach \emph{in situ} to make sure that a patient recovers in the least possible amount of time is of paramount importance. 

In the turn of the twenty-first century, robots have become so advanced that they can easily outstrip humans in terms of accuracy and precision due to their far superior computation capabilities. But, when it comes to the context of medicine, experience and cognitive abilities are far more important than computation efficiency. One reason for this is that in a lot of scenarios, the imaging modality which can be safely used during a surgery, it is not always conceivable to equip the robot with every possible byte of information to accurately visualize the tools that it are being used inside the human body. 

There are several areas of surgery which have a pressing need for accuracy and precision. A very specific example of this is biopsy-collection. The surgeon needs to know exactly where to make the incision and extract biological tissue samples and at the same time be precise enough to repeat it during future check-ups. Robot enhanced surgical equipment could provide a huge advantage in this scenario since it is very easy to know the exact location of the equipment connected with a robot in three dimensions (a feat which can simply not be replicated by humans, no matter how good their hand-to-eye coordination is). This enables robots to perform biopsies and punctures based on pre-evaluated scans much more accurately in comparison with humans. If these procedures can be entirely offloaded to robots, surgeons can focus on the more important tasks in the surgical procedure.

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\section{Problem Statement}

This study aims to develop an algorithm which can help track the tip of a surgical needle to facilitate quick and precise responses during a surgery, both by human and robotic surgeons. The surgical needle can thus be tracked while it is being inserted into a subject taking into account different effects like deformation caused by tissue resistance. This would facilitate accurate positioning of the robotic surgical system and it would be able to perform biopsies and punctures more accurately.

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\section{State of the Art}

During certain surgical procedures (like neurosurgery, cardiac surgery, prostrate therapy, surgical oncology to name a few), it is vital for the surgeon to know the exact position of the tools used in surgery since accuracy is a major factor. Towards this end, Black et al. \cite{Black1997} and Schweiger et al. \cite{Schweiger2000} studied the use of MRI and CT respectively. Although promising, these modalities have some major drawbacks (slow acquisition time, ionizing radiation, incapability of using metal tools, etc.) that make them unsuitable to monitor procedures that require constant visual feedback. In comparison, ultrasound is an inexpensive, flexible and harmless modality which can acquire images fairly quickly; these characteristics make ultrasound an ideal candidate for assisting surgeons during interventional procedures. An example of ultrasound data is shown in Figure 1.1. It is a three-dimensional ultrasound image of a phantom that mimics the human abdomen.

\begin{figure}[h]
	\label{us_ex}
	\centering
	\subfloat[Without needle \label{needle_without}]{\includegraphics[height=5.5cm]{figures/ultrasound_example_1}}
	\quad
	\subfloat[With needle \label{needle_with}]{\includegraphics[height=5.5cm]{figures/ultrasound_example_2}}
	\caption{Exemplary ultrasound images without and with needle presence.}
\end{figure}

The signal-to-noise ratio (SNR) is an excellent measure of image quality. Though CT and MRI take a lot of time to acquire the image, their quality is extremely good (with very high SNR values). In the case of ultrasound, though, the SNR experiences a dramatic drop. Even highly trained and experienced ultrasound operators can not obtain images that have comparatively high SNR due to physical constraints on the part of the transducer. Thus, recognizing geometric structures accurately can give surgeons a much deeper insight. Needle detection in particular is very important \cite{Hong2004,Takes1998} since it can significantly improve accuracy and reduce operating time and it will also make robotic assistance possible. The key concern for this would be the localization of the needle tip \cite{Hong2004}.

Several studies have been carried out to tackle this problem (an illustrative reference has been shown in Figure 1.2) that use some type of extraneous tracking information (for example, electromagnetic trackers placed at the needle tip \cite{Barva2004,Chatelain2013} or serrating the needle tip \cite{Ding2006}) to localize the position of the needle tip. Although these methods work reasonably well, the time required for a cost minimizer like RANSAC \cite{Barva2005} to accurately estimate the position increase along with the accuracy demand. For a robotic system to work well, computational efficiency and accuracy are of the highest priority; and thus these methods fall short of the real-time requirement. To solve this, we have proposed a new method in this study where we try to use very simple detectors combined with particle filters to track the position of a needle in an ultrasound volume without using any extraneous tracking information (i.e., a pure vision-based approach).


In the study conducted by Mung et al. \cite{Mung2011}, a needle tip with a special frequency emitter has been used in tandem with a calibrated transducer system to track the needle. Clear Guide Medical\textsuperscript{\textregistered} has commercialized the work done in \cite{ClearGuide}, where a stereo-camera system is used to track any rigid instrument relative to the US transducer. Other studies \cite{Barva2005,Chatelain2013,Uhercik2010} have tackled the problem of needle tip localization using electromagnetic (EM) trackers for the initial localization. While these methods are able to tackle the problem sufficiently well, they require modification of the needle to accommodate their respective tracking systems. Within the category of image-based methods, three main types of detectors (RANSAC, Hough transform and Radon transform) have been used to tackle the needle tip localization problem. Although these methods achieve an acceptable tracking accuracy, the time required for a cost minimizer like RANSAC \cite{Barva2005,Chatelain2013} to accurately estimate the position increases along with the accuracy demand. For a robotic system to work well, processing time and accuracy are of the highest priority.

In the case of Uhercik et al. \cite{Uhercik2010} a threshold is applied to the US volume, and then RANSAC is used to localize the needle axis using \emph{a priori} information about voxel intensities and the background. The study conducted by Chatelain et al. \cite{Chatelain2013} smoothened the resulting detection using a Kalman filter, which is subsequently used to predict the next position of the needle and to reduce the volume of interest to process. The needle tip position is then determined by analyzing the intensity drop along the axis. In Ren et al. \cite{Ren2011}, the plane containing the tool is estimated in an iterative manner using RANSAC, and the extended Kalman filter is utilized to track the needle tip. Since the deformation model of the tool is pre-determined, it can lead to increased error when a different type of tool needs to be tracked. In the study conducted by Barva et. al. \cite{Barva2005}, the US volume is segmented by applying linear thresholds and processing the voxels using the randomized version of RANSAC. The needle is modeled using a cubic curve, which is used to fit the shape of the detected needle using a least squares fitting criteria. The localization of its tip is done by maximizing the distance between the detected needle voxels. Of all these studies, \cite{Uhercik2010} achieves significantly better performance, with average error cited as less than 1 mm in position and $1^{\circ}$ for orientation while keeping the processing time being around 1 second per volume for an US image of resolution $273\times383\times208$.

In the study presented by Aboofazeli et al. \cite{Aboofazeli2009}, the filtered 3D US image is projected onto a 2D plane, on which the needle is first detected using Hough transform (HT) and then segmented. This technique is able to detect needles without any previous knowledge about the needle shape or any extraneous tracking information. Though the method presented there is very encouraging, the error cited (3.2\% of the whole needle length) remains too high for a robotic interventional system. Also, the processing time (3 seconds per volume) is considerably higher than standard volume acquisition rates in current US machines. In Ding et al. \cite{Ding2006}, a similar projection-based approach is used, though the projections are taken approximately orthogonal to the needle orientation. The needle is segmented from these projections and is then reconstructed. A Kalman filter is used to track the noise level across the volumes leading to increased accuracy (around 1.03 mm) at the cost of higher processing time (280 seconds per volume).

This study investigates a new image-based approach for robot-assisted interventional surgery. We propose to use a combination of robust detectors (such as HT) and particle filtering to track the position of a needle in an US volume without using any extraneous tracking information. This would enable us to use the routine US and surgical hardware without any modification. Thus, the medical professionals can keep using the tools which they are comfortable with while not compromising accuracy. Also, since the proposed method uses no \emph{a priori} information about the needle geometry, it can track flexible needles that undergo deformation after interaction with biological tissue. 


\begin{figure}[h]
	\label{reference}
	\centering
	\includegraphics[height=6cm]{figures/detectors}
	\caption{Different detectors that have been used prior to this study to solve the problem of Needle tracking.}
\end{figure}